R计算和绘图

Question

我是R的新人。

我有附加的数据范围，这是镇议会问题的清单：

SNO  Dept  FeedbackDate  ClosedDate2        SubCategory1          SubZone
  1   BTA    23/11/2012    4/12/2012  Permission-to-Park   TOWNSVILL EAST
  2   RTA    23/12/2012    4/13/2012              Rodent  TOWNSVILL SOUTH
  3   MTA    23/12/2012    4/16/2012           ConductVL  TOWNSVILL SOUTH

我想获得一些见解

1）哪个部门对不同子区域中的同一子类别具有更高的解决时间（ClosedDate2 - FeedbackDate）。

2）不同子区域中重复子类别的解析时间。

Answer 1

dplyr和ggplot2：

的情况如何

我已经编辑/制作了一个更大的数据集来展示它是如何工作的：

dat<-data.frame(SNO=1:100,
           Dept=sample(c("BTA","RTA","MTA"),100,T),
           FeedbackDate=as.Date("2012/12/23"),
           ClosedDate2=as.Date("2012/12/23")+ceiling(runif(100)*20),
           SubCategory1=sample(c("Permission-to-Park","Rodent","ConductVL"),100,T),
           SubZone=sample(c("TOWNSVILL EAST","TOWNSVILL SOUTH"),100,T))

require(ggplot2)
require(dplyr)     #for aggregation

dat.sum<-group_by(dat, SubCategory1, SubZone, Dept) %.%  # group by SC1, SZ and Dept comb
  summarise(AvgResTime=mean(ClosedDate2-FeedbackDate))   # calculate average of closure date for each

ggplot(dat.sum) + #use aggregated dat
  geom_point(aes(x=SubCategory1, y=AvgResTime,color=SubZone),size=10) + # color points by Zone
  facet_wrap(~ Dept) +                                                  # one face per department
  theme(axis.text.x = element_text(angle = 90, hjust = 1))              # rotate x axis text

使用plyr更新了以前的版本

require(plyr)     #for aggregation
dat.sum<-ddply(dat,.(SubCategory1, SubZone, Dept),summarise,AvgResTime=mean(ClosedDate2-FeedbackDate))

ggplot(dat.sum) + #use aggregated dat
  geom_point(aes(x=SubCategory1, y=as.integer(AvgResTime),color=SubZone),size=10) + # color points by Zone
  facet_wrap(~ Dept) +                                                  # one face per department
  theme(axis.text.x = element_text(angle = 90, hjust = 1))              # rotate x axis text